Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network—Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes
cg.contact | Jennifer.Woodward@ars.usda.gov | en_US |
cg.contributor.center | International Center for Agricultural Research in the Dry Areas - ICARDA | en_US |
cg.contributor.center | International Livestock Research Institute - ILRI | en_US |
cg.contributor.center | Ethiopian Institute of Agricultural Research - EIAR | en_US |
cg.contributor.center | Food and Agriculture Organization of the United Nations - FAO | en_US |
cg.contributor.center | Lilongwe University of Agriculture & Natural Resources - LUANAR | en_US |
cg.contributor.center | Cornell University - CORNELL | en_US |
cg.contributor.center | Association for Strengthening Agricultural Research in Eastern and Central Africa - ASARECA | en_US |
cg.contributor.center | United States Department of Agriculture, Agricultural Research Service - USDA-ARS | en_US |
cg.contributor.center | University of Natural Resources and LIfe Science - BOKU | en_US |
cg.contributor.center | Virginia State University - VSU | en_US |
cg.contributor.center | Ministry of Livestock and Fisheries -Tanzania | en_US |
cg.contributor.center | Nelson Mandela African Institute of Science and Technology - NM - AIST | en_US |
cg.contributor.center | National Research Center - NRC | en_US |
cg.contributor.center | National Animal Genetic Resources Centre and DataBank - NAGRC & DB | en_US |
cg.contributor.center | George Mason University, College of Science, School of Physics, Astronomy, and Computational Sciences - GMU-CoS-SPACS | en_US |
cg.contributor.center | Acceligen Inc | en_US |
cg.contributor.center | George Mason University, College of Science, School of Systems Biology College of Science - GMU-CoS-SSBCS | en_US |
cg.contributor.center | National Center for Applied Research in Rural Development - CENRADERU | en_US |
cg.contributor.center | Animal Production Research Center, Department of Animal Genetic Resources Development - APRC-DAGRD | en_US |
cg.contributor.center | Institut d’Économie Rurale | en_US |
cg.contributor.center | Mozambique Institute of Agricultural Research, Directorate of Animal Science - IIAM-DAS | en_US |
cg.contributor.crp | Resilient Agrifood Systems - RAFS | en_US |
cg.contributor.funder | United States Department of Agriculture, Agricultural Research Service - USDA-ARS | en_US |
cg.contributor.initiative | Sustainable Animal Productivity | en_US |
cg.contributor.project-lead-institute | International Center for Agricultural Research in the Dry Areas - ICARDA | en_US |
cg.creator.id | Getachew, Tesfaye: 0000-0002-0544-6314 | en_US |
cg.creator.id | Haile, Aynalem: 0000-0001-5914-0487 | en_US |
cg.creator.id | Rischkowsky, Barbara: 0000-0002-0035-471X | en_US |
cg.identifier.doi | https://dx.doi.org/10.3389/fgene.2023.1200770 | en_US |
cg.isijournal | ISI Journal | en_US |
cg.issn | 1664-8021 | en_US |
cg.journal | Frontiers in Genetics | en_US |
cg.subject.actionArea | Resilient Agrifood Systems | en_US |
cg.subject.impactArea | Nutrition, health and food security | en_US |
cg.subject.impactArea | Poverty reduction, livelihoods and jobs | en_US |
cg.subject.sdg | SDG 1 - No poverty | en_US |
cg.subject.sdg | SDG 13 - Climate action | en_US |
cg.volume | 14 | en_US |
dc.contributor | Kinser, Jason | en_US |
dc.contributor | Huson, Heather | en_US |
dc.contributor | Sonstegard, Tad | en_US |
dc.contributor | Sölkner, Johann | en_US |
dc.contributor | Vaisman, Iosif | en_US |
dc.contributor | Boettcher, Paul | en_US |
dc.contributor | Masiga, Clet Wandui | en_US |
dc.contributor | Mukasa, Christopher | en_US |
dc.contributor | Guangul, Solomon | en_US |
dc.contributor | Agaba, Morris | en_US |
dc.contributor | Ahmed, Sahar | en_US |
dc.contributor | Maminiaina, Oliver | en_US |
dc.contributor | Getachew, Tesfaye | en_US |
dc.contributor | Gondwe, Timothy | en_US |
dc.contributor | Haile, Aynalem | en_US |
dc.contributor | Hassan, Yassir | en_US |
dc.contributor | Kihara, Absolomon | en_US |
dc.contributor | Kouriba, Aly | en_US |
dc.contributor | Mruttu, Hassan | en_US |
dc.contributor | Mujibi, Denis | en_US |
dc.contributor | Nandolo, Wilson | en_US |
dc.contributor | Rischkowsky, Barbara | en_US |
dc.contributor | D. Rosen, Benjamin | en_US |
dc.contributor | Sayre, Brian | en_US |
dc.contributor | Taela, Maria | en_US |
dc.contributor | Van Tassell, Curtis P. | en_US |
dc.creator | Woodward-Greene, Jennifer | en_US |
dc.date.accessioned | 2023-12-29T16:06:15Z | |
dc.date.available | 2023-12-29T16:06:15Z | |
dc.description.abstract | Introduction: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) is an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The AGIN-ICP collects images to extract several phenotype measures including health status indicators (anemia status, age, and weight), body measurements, shapes, and coat color and pattern, from digital images taken with standard digital cameras or mobile devices. This strategy is to quickly survey, record, assess, analyze, and store these data for use in a wide variety of production and sampling conditions. Methods: The work was accomplished as part of the multinational African Goat Improvement Network (AGIN) collaborative and is presented here as a case study in the AGIN collaboration model and working directly with community-based breeding programs (CBBP). It was iteratively developed and tested over 3 years, in 12 countries with over 12,000 images taken. Results and discussion: The AGIN-ICP development is described, and field implementation and the quality of the resulting images for use in image analysis and phenotypic data extraction are iteratively assessed. Digital body measures were validated using the PreciseEdge Image Segmentation Algorithm (PE-ISA) and software showing strong manual to digital body measure Pearson correlation coefficients of height, length, and girth measures (0.931, 0.943, 0.893) respectively. It is critical to note that while none of the very detailed tasks in the AGIN-ICP described here is difficult, every single one of them is even easier to accidentally omit, and the impact of such a mistake could render a sample image, a sampling day’s images, or even an entire sampling trip’s images difficult or unusable for extracting digital phenotypes. Coupled with tissue sampling and genomic testing, it may be useful in the effort to identify and conserve important animal genetic resources and in CBBP genetic improvement programs by providing reliably measured phenotypes with modest cost. Potential users include farmers, animal husbandry officials, veterinarians, regional government or other public health officials, researchers, and others. Based on these results, a final AGIN-ICP is presented, optimizing the costs, ease, and speed of field implementation of the collection method without compromising the quality of the image data collection. | en_US |
dc.format | en_US | |
dc.identifier | https://mel.cgiar.org/reporting/downloadmelspace/hash/fb0760256d555f3fab5bd26e4c95cdbe/v/451fe3fee59cf8555ada45167a9cc19e | en_US |
dc.identifier.citation | Jennifer Woodward-Greene, Jason Kinser, Heather Huson, Tad Sonstegard, Johann Sölkner, Iosif Vaisman, Paul Boettcher, Clet Wandui Masiga, Christopher Mukasa, Solomon Guangul, Morris Agaba, Sahar Ahmed, Oliver Maminiaina, Tesfaye Getachew, Timothy Gondwe, Aynalem Haile, Yassir Hassan, Absolomon Kihara, Aly Kouriba, Hassan Mruttu, Denis Mujibi, Wilson Nandolo, Barbara Rischkowsky, Benjamin D. Rosen, Brian Sayre, Maria Taela, Curtis P. Van Tassell. (6/9/2023). Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network—Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes. Frontiers in Genetics, 14. | en_US |
dc.identifier.status | Open access | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.11766/68957 | |
dc.language | en | en_US |
dc.publisher | Frontiers Media | en_US |
dc.rights | CC-BY-4.0 | en_US |
dc.source | Frontiers in Genetics;14,(2023) | en_US |
dc.subject | community-based breeding program | en_US |
dc.subject | african goat improvement network | en_US |
dc.subject | image collection protocol | en_US |
dc.subject | livestock phenotypes | en_US |
dc.title | Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network—Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes | en_US |
dc.type | Journal Article | en_US |
dcterms.available | 2023-09-06 | en_US |
dcterms.issued | 2023-09-06 | en_US |
mel.impact-factor | 3.7 | en_US |